How to Use the Bringg MCP in OpenAI Agents SDK

Directly manage your fleet and delivery tasks inside the OpenAI Agents SDK for production-grade logistics control.

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Connect Bringg MCP to OpenAI Agents SDK

Create your Vinkius account to connect Bringg to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Bringg with OpenAI Agents SDK

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Control delivery tasks with OpenAI Agents SDK

Trigger `create_delivery_task` or `cancel_task_dispatch` directly from your Python agent logic. You get full command over your dispatch queue without leaving your IDE. Your agent handles state transitions using `force_task_start` and `force_task_complete`. It ensures your delivery lifecycle matches reality on the ground.

Manage your drivers and customers

Use `list_fleet_drivers` to see who is available. You can then use `assign_driver_to_task` to override automated routing when things get messy. Your agent checks historical records via `list_customer_crm`. It keeps your delivery data accurate by running `update_task_details` whenever notes change.

Monitor active logistics via MCP Server

Run `list_active_tasks` to get a snapshot of the current workload. It maps the SaaS dashboard state directly into your agent's context. Your code calls `get_task_timeline` to pull deep status updates. You track every move of the delivery chain in real time.

Setup guide

Set up Bringg MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Bringg tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Bringg tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Bringg tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Bringg Agent",
            instructions="You have access to Bringg tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Bringg MCP in OpenAI Agents SDK

Yes. You connect the Bringg MCP Server to your agent and invoke the dispatch tools directly. Your agent makes the calls based on your specific business rules.

The server forces specific task states like start or complete. This ensures your agent maintains an accurate view of delivery progress.

It is. You can list all active drivers and manually assign them to tasks. This gives your agent total control over fleet management.

You can use the cancel tool to remove the task from your schedule. The server updates the hub immediately to prevent further errors.

Your customer delivery records stay within the Bringg ecosystem. The MCP Server acts as a secure bridge, and Vinkius uses ephemeral isolated environments to ensure your data stays private during execution.

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